Abstract
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines.- Anthology ID:
- 2020.acl-main.352
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3804–3810
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.352
- DOI:
- 10.18653/v1/2020.acl-main.352
- Cite (ACL):
- Lixing Zhu, Yulan He, and Deyu Zhou. 2020. Neural Temporal Opinion Modelling for Opinion Prediction on Twitter. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3804–3810, Online. Association for Computational Linguistics.
- Cite (Informal):
- Neural Temporal Opinion Modelling for Opinion Prediction on Twitter (Zhu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.352.pdf